Power assisted automatic supervised classifier creation tool for semiconductor defects

ABSTRACT

A method and system that optionally allows a user to view image defects organized by natural groupings based on features of the images. The natural groupings make it easier for the user to organize some or all of the images into classes in a training set of images. A feature vector is extracted from each image in the training set and stored, along with its user-specified class, for use by an automatic classifier software module. The automatic classifier uses the stored feature vectors and classes to automatically classify images not in the training set. If the automatically classified images do not match images manually classified by the user, the user modifies the training set until a better result is obtained from the automatic classifier. The system can provide feedback to an inspection system designed to aid in the setup and fine-tuning of the inspection system.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application Ser. No. 60/167,955 entitled “power AssistedAutomatic Supervised Classifier Creation Tool for SemiconductorDefects,” of Bakker, Banerjee, and Smith et al., filed Nov. 29, 1999.

The following applications are related to this application and areherein incorporated by reference:

-   -   1. U.S. application Ser. No. 08/958,288 of Hardikar et al.,        filed Oct. 27, 1997.    -   2. U.S. application Ser. No. 08/958,780 of Hardikar et al.,        filed Oct. 27, 1997.

The following U.S. patent is related to this application and is hereinincorporated by reference:

-   -   1. U.S. Pat. No. 5,226,118 to Baker et al., issued Jul. 6, 1993.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to software programs and, moregenerally, to software-controlled optical, ebeam, or other types ofinspection systems for semiconductor wafers.

2. Description of Background Art

As new materials, methods, and processes are introduced intosemiconductor manufacturing, new defects are emerging in themanufacturing process that can greatly impact yield. These changesrequire chipmakers to adopt new technologies to detect and classifythese yield-limiting defects more quickly, accurately and consistentlyin order to tighten their manufacturing processes and accelerate theiryield-learning curve. At the same time, shrinking product lifecycles andaccelerated time-to-market requirements are forcing fabrication plantsto speed their product ramp ups for new products to meet theirprofitability objectives. This, in turn, is driving the need for fasterautomatic defect classification (ADC) setup to ensure fabrication plantscan reap the benefits of ADC without slowing the ramp process.

Currently existing optical inspection systems with automatic defectclassification require human beings to visually inspect semiconductorwafers for suspected defects and to classify the types and causes of thedefects in order to set up the automatic classification system. Toperform this classification, a human being must sort through hundreds ofimages that are presented in random order. This process takes many hoursand increases the cost of production. Moreover, even skilled humanoperators are somewhat slow and prone to error.

SUMMARY OF THE INVENTION

The described embodiments of the present invention receive images ofdefects and aid a user in classifying the types of defects represented.A graphical user interface allows a human user to manually classify thedefect images via Automatic Supervised Classifier software and furtherallows the user to contrast his manual classifications with theclassifications determined by Automatic Supervised Classifier software.In order to create an Automatic Supervised Classifier for semiconductordefect classification, the human user has to perform two manual tasks:

(i) Creation of a good Classification Scheme (which images the user willplace into which classes).

(ii) Creation of a good training set of examples for the AutomaticSupervised Classifier with this Classification Scheme.

The described embodiments of the invention provide a tool to help thehuman user achieve both these objectives in record time.

The embodiments described herein contain four main components thatseamlessly interact with one another:

(i) Image Gallery: This is a graphical interface to display a list ofimages in an organized fashion.

(ii) Dynamic Automatic Supervised Classifier: Given a list of defects,the user is allowed to manually classify or train any set of defects.The rest of the defects are dynamically classified accordingly and theoverall performance of the resulting classifier is calculated.

(iii) Dynamic Classifier Controls and Performance Tools: This allows theuser to visualize and optimize the parameters of the classifier evenfurther. As in (ii), the resulting performance is immediately visible.

(iv) Unsupervised Automatic Classifier (Natural Grouping): This groupsthe images in a way that allows the user to visualize the layout andstructure of the feature space in terms of defect images, and assiststhe user in creating both a good classification scheme and a goodtraining set of examples.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1( a) is a block diagram showing an overview of a semiconductoroptical, ebeam, or other types of inspection systems.

FIG. 1( b) is a block diagram showing an overview of a semiconductoroptical, ebeam, or other types of inspection systems using the presentinvention for inspection set-up.

FIG. 2 is a block diagram showing the interaction of a human user withsections of an embodiment of defect classifier software.

FIG. 3 shows an interface generated by defect classifier software inaccordance with a preferred embodiment of the present invention.

FIG. 4( a) shows an example of a confusion matrix in the user interfacewhere the manual and automatic classification are in agreement.

FIG. 4( b) shows an example of the confusion matrix in the userinterface where the manual and automatic classification are not inagreement.

FIGS. 5( a) and 5(b) shows examples of an interface that allows the userto display in sorted order the images in the working set and in thetraining set.

FIG. 6( a) is a flow chart showing a method for natural grouping ofimages in the working set.

FIG. 6( b) shows images organized and displayed in their natural groups.

FIG. 7 shows an expanded view of a training area in the user interface.

FIG. 8 shows an example user interface used in a preferred embodiment ofthe present invention.

FIG. 9 shows an additional user interface for the Classifier Function.

FIGS. 10( a), 10(b), and 10(c) are block diagrams of systems inaccordance with the present invention distributed over a network, suchas the internet or an intranet.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The described embodiments of the present invention aid a user inclassifying defect images input to the system from an outside source.For example, the images can be received from a scanning electronmicroscope (SEM), a defect inspection system, such as KLA-Tencor's 2132inspection tool, or any similar instrument used to generate images anddetect errors therein. In certain embodiments, the images can come frommore than one type of input source. For example, images may be receivedfrom both an SEM and a 2132 inspection tool. Different types of imagesreceived from different sources can aid in defect classification, sincedifferent types of images produce more information is providedconcerning the defects to be classified. The images also can be pointlocations on wafers.

FIG. 1( a) is a block diagram showing an overview of a semiconductoroptical, ebeam, or other types of inspection systems. As discussedabove, defect images are preferably received from an outside source,such as an optical, ebeam, or other types of inspection systems 102, anSEM or a 2132 inspection tool. The defect images tell a classificationsystem 104 that defects have occurred, but does not classify the defectsas to types or causes. After the defects are classified, as describedbelow, they are sent to an analyzer 106, such as KLA-Tencor's KLAritysystem, an example of which is described in the above-referenced U.S.application Ser. Nos. 08/958,288 and 08/958,780.

FIG. 1( b) is a block diagram showing an overview of a semiconductoroptical, ebeam, or other types of inspection systems using the presentinvention for inspection set-up. As discussed above, defect images arepreferably received from an outside source, such as an optical, ebeam,or other types of inspection systems 102, an SEM or a 2132 inspectiontool. These images can relate to etch, photolithography, deposition,CMP, or to some other manufacturing process. The defect images tell aclassification system 104 that defects have occurred, but does notclassify the defects as to types or causes. After the defects areclassified, as described below, they are sent to an analyzer 106, suchas KLA-Tencor's KLArity system, an example of which is described in theabove-referenced U.S. application Ser. Nos. 08/958,288 and 08/958,780.The output of the analyzer 106 is used as feedback to fine tune theinspection system.

For example, depending on the number of errors found and the accuracydesired, the inspection system may be fine tuned to raise or lower thesensitivity of the system. For example, if the system is finding toomany errors or errors that are not relevant to the particularmanufacturing process, the system may be fine tuned to lower itssensitivity, resulting in fewer errors detected. As another example, ifnot enough errors are being detected or if errors of a certain type arenot being detected, the inspection system may be adjusted to become moresensitive, so as to detect data that will result in the detection ofmore errors or of errors of a desired type. In certain embodiments, ithas been observed that as sensitivity is increased, error detectionincreases exponentially. In such a system, feedback to the ADC can beused to control inspection parameters including but not limited to:illumination, sensitivity or sensing (optical, ebeam, etc), threshold ofdetection, filtering, and/or polarization.

In another embodiment, the feedback is used to control the manufacturingprocess. For example, feedback from the analysis portion could be usedto shutdown the process or a certain machine if too many errors aredetected from that machine. Similarly, the feedback could be used tore-route lots to machines or processes with the lowest errors rates oneither a static or a dynamic basis.

In another embodiment, the inspection and analysis/classificationprocess is performed in real-time during the inspection process insteadof as a separate process. (An example of this is shown in the system ofFIG. 10( c)). In such a system, inspection system 1052 is shown insidethe system 1004 to indicate that it is part of the same system as theclassifier 1056.

FIG. 2 is a block diagram showing the interaction of a human user withan embodiment of defect classifier software. A working set 208 ofimages, such a wafer defect images, is displayed for user review (asdiscussed below in connection with FIG. 3). A human user 210 can reviewthe defect images in the working set.

The human user can also request that the images be organized by naturalgrouping 212 and displayed according to this organization. The human canmanually classify the defect images into classes (also called “bins”)according to the human's understanding of the type of defect representedby the image. Currently, the extracted features of the defect images areused to naturally group the defect images, using a Kohonen mappingtechnique. Kohonen mapping is described, for example in T. Kohonen, “TheSelf-Organizing Map,” Proceedings of the IEEE, Vol. 78, 1990, pp.1464–1480, which is herein incorporated by reference. Other methods canbe used for natural grouping, such as K-means; the method described inN. Otsu, “A Threshold Selectron Method from Gray-Level Histrograms,”IEEE Trans. Systems, Man, and Cybernetics, Vol. SMC-9, 1979, pp. 62–66(which is herein incorporated by reference); or any other appropriatetechnique or method that groups defect images according to commonfeatures. In a described embodiment, both the natural grouping 212 andthe automatic classifier 204 use the same feature set.

In addition, the human user can select images from the working set to beplaced in a “training set” of images. The user then manually addsimages/defects to the class/bins of the training set. Features areextracted from the selected images and stored along with the class/binduring a “train classifier” operation. The classifier then classifies aset of images (such as the set W-T) and the user reviews the errorsfound in the classifier's decisions. For example, the user may view theconfusion matrix to determine where the classifier differed form theuser's classifications. The user then improves the training set byadding deleting, or reclassifying images via, e.g., a drag and dropinterface and reassesses the classifier's performance until asatisfactory result is achieved.

The images in the training set are sent to feature extractor software206, which extracts a set of predefined features for each image in thetraining set. The data structures storing a set of features for an imageis called the image's “feature vector.” A feature vector contains thevalues for each feature for a particular image.

The predefined features preferably extracted from the training setinclude, but are not limited to:

a) features extracted from an image, such as: size, brightness, color,shape, texture, moment of inertia, context, proximity to wafer featuresor other defects, connectivity to adjacent features or other defects,other yield relevant properties derived from the image (e.g. short,open, bridging, particles, scratches, etc.)

b) defect coordinates in wafers and spatial clusters of defectcoordinates in the case of spatial cluster analysis, and

c) other information pertaining to the defect that may be have beendeveloped a priori, including but not limited to image type informationsuch as in list a) and b), compositional or electrical informationderived from analytic techniques and information pertaining to theprocessing history, yield relevance or origins of the defects inquestion. It will be understood that any appropriate features can beextracted without departing from the spirit of the present invention.Examples of analytical techniques used to derive compositional orelectrical information are described in “Semiconductor Characterization:Present Status & Future Needs” ed. W. M. Bullis, D. G. Seiler, A. C.Diebold, American Institute of Physics 1996, ISBN 1-56396-503-8 whichcontains an overview of the myriad ways of analyzing defects and theiryield relevance and which is herein incorporated by reference.

Supervised Automatic Dynamic Classifier software 204 uses the extractedfeatures of the images in the training set to classify the images in theworking set (W) that were not selected by the user as part of thetraining set (T) (i.e., to classify the set of images W-T). In apreferred embodiment of the invention, the classifier 204 uses a nearestneighbor method in which the features are extracted from the image setW-T and each image in W-T is classified as belonging to a class. Ingeneral, an image in W-T belongs to the class whose members have featurevectors most closely resembling the feature vector of the image. Itshould be understood that other automatic classification methods couldbe used. For example, the features in the feature vectors could beweighted (either by the user or using a predefined weighting).

Once the images in the set W-T is classified by classifier 204, theresults of the automatic classification are compared with the results ofthe user's manual classification. If the user has classified any imagesthat are also classified by classifier 204, the results are compared andthe comparison displayed in a visual form to the user. The user may, atthis point, alter his classification scheme and make alterations to thetraining set of images accordingly. The user may also change his manualclassification if he decides that the automatic classification looksmore correct.

FIG. 3 shows an interface generated by defect classifier software inaccordance with a preferred embodiment of the present invention.Specifically, FIG. 3 shows an example of a “Smart Gallery” window inaccordance with a preferred embodiment of the invention. One of the mainpurposes behind the Smart Gallery window is to provide a gallery-basedclassification capability. The Smart Gallery allows the user to viewthumbnail images of the defects at adjustable sizes and resolutions,presenting a group of defects in an organized fashion. It also assiststhe user in the classification scheme creation by providing defects ingroups based on appearance.

The benefits of the Smart Gallery system include:

-   -   Defect sorting    -   Allowing the human user to perform quicker and more efficient        classification and organization of his manual sorting schemes    -   Faster manual sorting scheme creation    -   Shorter manual classification time    -   Better manual classification quality (repeatability)    -   Quicker, more effective classifier creation process

The window of FIG. 3 includes a tool bar 302, which contains commandsthat allow the user to open and save classes and defect images, to sortimages in the working set, and to create and manipulate the trainingset. A confidence setting area 304 allows the user to adjust theConfidence level, which is an adjustable setting of how close an unknowndefect can be to a training set. Values preferably range from 0 (loosestsetting) to 1 (tightest setting). Changes can be dynamically viewed inconfusion matrix 306.

Confusion matrix 306 is used to display the results of manual vs.automatic defect classification. A confusion matrix can be generated forboth a current set of images or an explicitly selected set. The manual(human) classification results are displayed on the X-axis and theautomatic classification by classifier 204 are displayed on the y-axis.Results of the comparison that are in agreement for all defect classes(where both manual and automatic classification results are inagreement) are displayed on the diagonal across the confusion matrix.

An area 308 displays the working set of images. These images may bedisplayed in unsorted order or may be sorted or arranged by naturalgrouping, at the choice of the user. The user preferably drags and dropsimages from the working set gallery 308 into the training set gallery310. Here, images in the training set are displayed arranged inuser-specified classes. Training set area 312 displays the classes (alsocalled “bins”) that contain the composition of the training set, definesgrouping, and allows the user to create new classes/bins. When this area312 is active, the user can create new classes/bins using the toolbar302.

Natural grouping matrix 314 allows the user to view how the images inthe working group are distributed in the natural groupings. The numberof images in a group is represented by a number in an element of thematrix 314. The user can click on an element in the matrix 314 to viewall defect images in a particular natural grouping.

In summary, the user can optionally indicate (e.g., via the toolbar or amenu item) that he wants the working set images sorted in naturalgroupings. The user then drags and drops the images from area 308 intothe classes/bins of the training set 310/312 and indicates a “training”function. The training function stores the feature vectors of theuser-selected images of the training set and stores them in connectionwith the classes/bins. Once the training set is indicated, the automaticclassifier classifies the remaining images. The classifier 204 can alsouse the training set to classify some other set of images. In differentembodiments, classifier 204 can either run in the background,reclassifying images whenever the training set is changed, or it can berun explicitly by the user. The result of the classifier 204 is comparedto any manual classification done by the user and the comparison resultsare displayed on the confusion matrix. The user can then add or subtractimages to or from the training set via 310 of FIG. 3 in accordance withthe contents of the confusion matrix.

The images can be automatically grouped according to invariant coreclasses, such as those shown in PCT Publication No. WO 00/03234(inventor: Ben-Porath et al.), published Jan. 20, 2000, which is hereinincorporated by reference. The results of the ADC process could also beincorporated in an overall fab yield management system, such as thatshown in PCT Publication No. WO 99/59200 (inventor: Lamey et al.),published Nov. 18, 1999, which is herein incorporated by reference. Thisapplication also incorporates by reference PCT Publication No. WO99/67626 (inventor: Ravid) published Dec. 29, 1999.

FIG. 4( a) shows an example of confusion matrix 306 where the manual andautomatic classification are in agreement (see diagonal elements 402).In the example, one image is agreed to be in class “3”, three images areagreed to be in class “2” and one image is agreed to be in class “1”.Clicking on a “Correct” button next to the matrix will cause results inagreement to be highlighted. Clicking on a “known errors” button willcause results not in agreement to be highlighted. Clicking on the“image” button allows the user to view the images that were used togenerate a particular element in the matrix.

FIG. 4( b) shows an example of the confusion matrix 306 where the manualand automatic classification are not in agreement. Element 452 is anon-zero element off the diagonal of the matrix.

FIGS. 5( a) and 5(b) shows respective examples of an interface 502, 552that allows the user to display in sorted order the images in theworking set and in the training set. The images are preferably sorted bysuch factors as lot number, manual bin, suggested bin, and size.

FIG. 6( a) is a flow chart showing a method for natural grouping ofimages in the working set 308. In element 602, images are captured fordefect images. Features are extracted from the images in element 604.The extracted features are input to a natural grouping method 606, whichcan be any appropriate method. In the described embodiment, the featurevectors of the images are grouped using a known Kohonen mappingtechnique. In the described embodiment, the Kohonen map is seeded withnon-random numbers to improve stability of the grouping and to make thegrouping repeatable. In some embodiments, the images are displayed intheir natural groups (also called clusters), as shown in FIG. 6( b). Inother embodiments, the images are arranged to reflect the actualKohonnen maps layout.

Certain embodiments use a Spatial Signature Analysis (SSA) Technique, asdescribed in 1) http://www-ismv.ic.ornl.gov/projects/SSA.html; 2)Gleason S. S., Tobin K. W., & Karnowski, T. P., “Spatial SignatureAnalysis of Semiconductor Defects for Manufacturing Problem Diagnosis”,Solid State Technology, July, 1996; 3) http://www.dym.com/ssa.htm; 4)http://www.electroglas.com/products/knights_datasheets/spar_ds.htm; and5) http://www.ornl.gov/Press_Releases/archive/mr19980804-00.html, whichare herein incorporated by reference.

In addition, in certain embodiments, the analysis and classification arenot limited to images, but can be performed on clusters themselves. Insuch an embodiment, the classifier receives “cluster-based features”instead of raw images as described in T. P. Karnowski, K. W. Tobin, S.S. Gleason, Fred Lakhani, SPIE's 24th Annual International Symposium onMetrology, Inspection and Process Control for Microlithography XIII,Santa Clara Convention Center, Santa Clara, Calif., February, 1999,which is herein incorporated by reference. Such a system appliesgrouping and Kohonen mapping to clusters instead of to raw images. Fornon-image data, clustering is gathered by EDS specifiers (using an x-raysystem for analysis in an e-beam system) or by SSA analysis.

FIG. 7 shows an expanded view of training area 312. The user can dragimages from the working group 308 into the displayed groups in area 310or into the classes/bins of area 312. In the displayed embodiment, eachclass/bin has a class code, a group code (reflecting its natural group)and a number of defect images currently assigned to the class/group. Theuser can, of course, add and delete classes/bins as he wishes (e.g., viathe toolbar).

If the user wants to add a new class/bin, the class/bin is added. Otherwise, an existing class/bin is opened. The user then manually addsimages/defects to the class. Features are extracted from the selectedimages and stored during a “train classifier” operation (e.g., via thetoolbar). The classifier then classifies a set of images (such as theset W-T) and the user reviews the errors found in the classifier'sdecisions. For example, the user may view the confusion matrix todetermine where the classifier differed form the user's classification.The user than improves the training set by adding deleting, orreclassifying images via, e.g., a drag and drop interface and reassessesthe classifier's performance until a satisfactory result is achieved.

FIG. 8 shows an example user interface that includes a “Smart Gallery”setup function 802, an Auto Classifier Creation function 804, and aClassifier function 806. The Smart Gallery setup function leads to theuser interface of FIG. 3. The classifier function leads to the userinterface of FIG. 9. “Smart Gallery is a trademark of KLA-TencorCorporation.

FIG. 9 shows an additional user interface for an embodiment of theAutomatic Classifier Function. This embodiment is an alternative to thetoolbar-driven, drag and drop method. Using this interface, a user canadd defect images to the training set 902 and specify the features toextract for natural grouping and for the feature extractor of theclassifier 904. The user can specify the number of features to extract(here, 80). When the user selects a Train button 906, the features ofthe images in the training set are extracted and saved as featurevectors for each image. The class/bin of each image is saved inassociation with the feature vector.

The user can set filters 908 on the images, removing certain groups,images, and types of images from the features extraction process. Theuser can also adjust the confidence of the feature method methods usedby the classifier 204 using button 910.

When the user clicks Test (Training set) button 912, the classifier 204classifies the set of images W-T into the bins in the training set inaccordance with the feature vectors of the images in the training set.

FIGS. 10( a) and 10(b) are block diagrams of systems in accordance withthe present invention distributed over a network; such as the Internetor an intranet. In FIG. 10( a), an optical, ebeam, or other types ofinspection systems 1002, a classifier 1004/104, and an analysis system1006 (see FIG. 1) are distributed over the network. In FIG. 10( b),elements of classifier 1056/204, and a feature extractor 1058, aredistributed over the network. Natural grouping process 1054 receives asinputs the features of the working set and outputs the natural groupingof the working set. Automatic supervised classifier 1056 receives thefeatures and classes of the training set and the features of defectimages, while outputting the classes of the defect images beingclassified. Feature extractor 1058 receives images and outputs featuresof the images.

FIG. 10( b) also shows an embodiment in which the classifier receivestool history 1005 as an input. Tool history includes, for example, themaintenance history of the tools or machine performing the inspectionprocess and/or the manufacturing process. If the tool has beenmaintained according to its suggested maintenance schedule, its data maybe weighted more than data from an unmaintained tool. Tool History 1055may also include a threshold of inspection value, indicating thatmaintenance must be found in order for the classifier to give credenceto the data from that tool. This threshold may vary for individual toolsor may be the same for all the tools of a particular type or function.Tool history may also indicate, for example, whether two runs ofsemiconductors where taken from the same tool (or which tool they weretaken from). Thus, tool history 1055 may include, for example, equipmentIds. If it is known, for example, that Tool A has had problems in thepast, data from tool A may be treated differently than data from atrouble-free tool B.

As described above, FIG. 10( c) shows that the inspection andanalysis/classification process is performed in real-time during theinspection process instead of as a separate process. In such a system,inspection system 1052 is shown inside the system 1004 to indicate thatit is part of the same system as the classifier 1056. The systemutilizes review images (“patch” images) to effect real-time inspection.New inspection systems such as the KLA 2350 include ADC embedded insidethe image computer running in ‘real time’ during the inspection. Thissystem, called an iADC (integrated ADC) system, works by grabbing‘patches’ around the defect location during scan and performing ADC onthe defective pixels in these patches. All this is done in hardwareinside the inspector so that no addition throughput it required.

FIGS. 10( a), 10(b), and 10(c) each contain a dotted line 1003, 1053depicting that, in certain systems, the classifier can provide feedbacksignals to the inspection system, in a similar manner discussed above inconnection with FIG. 1.

It will be understood that various embodiments and alternations canexist without departing from the spirit and scope of the invention. Forexample, the concept of the invention can be extended to includeautomatically sorting images in the background (for example whilerunning defect analysis software) by defect type, and then displayingthe result as a wafer map with a distribution of each selected typeshown over the wafer map. This is just one way of using the output data.Defect location distribution can be helpful in identifying defectsource, so the ability to select similar defects (natural grouping)coupled with the ability to see their spatial distribution could bepowerful. A display can be included showing for each cluster in theKohonnen map a) a representative image, and b) a defect map showing thelocations of the defects in the cluster.

From the above description, it will be apparent that the inventiondisclosed herein provides a novel and advantageous system and method ofoptical inspection used to classify semiconductor defects.

1. A method for classifying a plurality of images, comprising: providinga working set of images; prior to a user performing any classificationof the working set of images, automatically sorting the working set ofimages into a plurality of groupings based on common features of theworking set of images and displaying such groupings; and afterautomatically sorting the working set of images into groupings,receiving input from the user to manually classify at least a subset ofthe working set of images facilitated by the displayed groupings.
 2. Themethod of claim 1, wherein the groupings are initially displayed as aplurality of elements, wherein each element specifies a number thatindicates how many of the working set of images are grouped together. 3.The method of claim 2, wherein each element is selectable by the user tothereby display the corresponding one or more working set of images thatare grouped together.
 4. The method of claim 1, further comprising:providing a training set, wherein the training set is formed from theuser's manually classified subset of the working set of images; andautomatically classifying the unclassified working set of images basedon a plurality of features extracted from the training set and theworking set of images and the user's manual classification of thetraining set.
 5. The method of claim 4, further comprising displaying avisual representation of a comparison between the automaticclassification and the manual classification performed by the user. 6.The method of claim 5, further comprising receiving input from the userto alter the training set based on the displayed visual representationof the comparison between the automatic classification and the manualclassification performed by the user so that the automaticclassification more closely matches the manual classification.
 7. Themethod of claim 5, further comprising receiving input from the user toalter one or more parameters of the automatic classification based onthe displayed visual representation of the comparison between theautomatic classification and the manual classification performed by theuser so that the automatic classification more closely matches themanual classification.
 8. The method of claim 7, wherein altering theone or more parameters of the automatic classification includesgraphically manipulating one or more images.
 9. The method of claim 4,further comprising automatically classifying a second working set ofimages based on a plurality of features extracted from the training setand the second working set of images and the user selected classes ofthe training set.
 10. The method of claim 4, wherein the common featuresused during the automatic sorting include one or more of a groupconsisting of size, brightness, color, shape, texture, moment ofinertia, context, proximity to wafer features, proximity to otherdefects, connectivity to adjacent features, connectivity to otherdefects, and yield relevant properties derived from the correspondingimage.
 11. The method of claim 1, wherein the common features usedduring the automatic sorting include defect coordinates in wafers. 12.The method of claim 1, wherein the common features used during theautomatic sorting include defect coordinates when spatial clusteranalysis is used.
 13. The method of claim 1, wherein common featuresused during the automatic sorting include information derived from oneof the processing history, yield relevance, and origins of defects. 14.The method of claim 1, wherein automatically sorting the working set ofimages includes using a Kohonen map technique.
 15. The method of claim14, wherein the Kohonen map is seeded with non-random numbers.
 16. Themethod of claim 14, wherein displaying the groupings includes arrangingthe working set of images to reflect the Kohonen map's layout.
 17. Themethod of claim 14, wherein displaying the groupings includes arrangingthe working set of images into natural groupings or clusters.
 18. Themethod of claim 1, wherein automatically sorting the working set ofimages includes using a K-means technique.
 19. The method of claim 1,wherein automatically sorting the working set of images includes using aspatial signature analysis technique.
 20. The method of claim 1, whereinautomatically sorting the working set of images is based on a pluralityof cluster features that each represent a cluster of the working set ofimages.
 21. The method of claim 4, further comprising receiving inputfrom the user or automatically receiving input for selecting a number offeatures to use for the automatic sorting and/or classification andapplying such selected feature number to the automatic sorting and/orclassification.
 22. The method of claim 1, wherein the common featuresused during the automatic sorting include tool history informationrelating to an inspection system or tool history information relating tothe past success rate of the classification step.
 23. The method ofclaim 1, wherein the working set of images originate from asemiconductor inspection process.
 24. A system for classifying aplurality of images, comprising: a software portion configured toprovide a working set of images; a software portion configured to priorto a user performing any classification of the working set of images,automatically sort the working set of images into a plurality ofgroupings based on common features of the working set of images anddisplaying such groupings; and a software portion configured to afterautomatically sorting the working set of images into groupings, receiveinput from the user to manually classify at least a subset of theworking set of images facilitated by the displayed groupings.
 25. Thesystem of claim 24, wherein the groupings are initially displayed as aplurality of elements, wherein each element specifies a number thatindicates how many of the working set of images are grouped together.26. The system of claim 25, wherein each element is selectable by theuser to thereby display the corresponding one or more working set ofimages that are grouped together.
 27. The system of claim 24, a softwareportion configured to: provide a training set, wherein the training setis formed from the user's manually classified subset of the working setof images; and automatically classify the unclassified working set ofimages based on a plurality of features extracted from the training setand the working set of images and the user's manual classification ofthe training set.
 28. The system of claim 27, a software portionconfigured to display a visual representation of a comparison betweenthe automatic classification and the manual classification performed bythe user.
 29. The system of claim 28, a software portion configured toreceive input from the user to alter the training set based on thedisplayed visual representation of the comparison between the automaticclassification and the manual classification performed by the user sothat the automatic classification more closely matches the manualclassification.
 30. The system of claim 28, a software portionconfigured to receive input from the user to alter one or moreparameters of the automatic classification based on the displayed visualrepresentation of the comparison between the automatic classificationand the manual classification performed by the user so that theautomatic classification more closely matches the manual classification.31. The system of claim 30, wherein altering the one or more parametersof the automatic classification includes graphically manipulating one ormore images.
 32. The system of claim 27, a software portion configuredto automatically classify a second working set of images based on aplurality of features extracted from the training set and the secondworking set of images and the user selected classes of the training set.33. The system of claim 27, wherein the common features used during theautomatic sorting include one or more of a group consisting of size,brightness, color, shape, texture, moment of inertia, context, proximityto wafer features, proximity to other defects, connectivity to adjacentfeatures, connectivity to other defects, and yield relevant propertiesderived from the corresponding image.
 34. The system of claim 24,wherein the common features used during the automatic sorting includedefect coordinates in wafers.
 35. The system of claim 24, wherein thecommon features used during the automatic sorting include defectcoordinates when spatial cluster analysis is used.
 36. The system ofclaim 24, wherein common features used during the automatic sortinginclude information derived from one of the processing history, yieldrelevance, and origins of defects.
 37. The system of claim 24, whereinautomatically sorting the working set of images includes using a Kohonenmap technique.
 38. The system of claim 37, wherein the Kohonen map isseeded with non-random numbers.
 39. The system of claim 37, whereindisplaying the groupings includes arranging the working set of images toreflect the Kohonen map's layout.
 40. The system of claim 37, whereindisplaying the groupings includes arranging the working set of imagesinto natural groupings or clusters.
 41. The system of claim 24, whereinautomatically sorting the working set of images includes using a K-meanstechnique.
 42. The system of claim 24, wherein automatically sorting theworking set of images includes using a spatial signature analysistechnique.
 43. The system of claim 24, wherein automatically sorting theworking set of images is based on a plurality of cluster features thateach represent a cluster of the working set of images.
 44. The system ofclaim 27, a software portion configured to receive input from the useror automatically receiving input for selecting a number of features touse for the automatic sorting and/or classification and applying suchselected feature number to the automatic sorting and/or classification.45. The system of claim 24, wherein the common features used during theautomatic sorting include tool history information relating to aninspection system or tool history information relating to the pastsuccess rate of the classification step.
 46. The system of claim 24,wherein the working set of images originate from a semiconductorinspection process.